fwildclusterboot - Fast Wild Cluster Bootstrap Inference for Linear Models
Implementation of fast algorithms for wild cluster bootstrap inference developed in 'Roodman et al' (2019, 'STATA' Journal, <doi:10.1177/1536867X19830877>) and 'MacKinnon et al' (2022), which makes it feasible to quickly calculate bootstrap test statistics based on a large number of bootstrap draws even for large samples. Multiple bootstrap types as described in 'MacKinnon, Nielsen & Webb' (2022) are supported. Further, 'multiway' clustering, regression weights, bootstrap weights, fixed effects and 'subcluster' bootstrapping are supported. Further, both restricted ('WCR') and unrestricted ('WCU') bootstrap are supported. Methods are provided for a variety of fitted models, including 'lm()', 'feols()' (from package 'fixest') and 'felm()' (from package 'lfe'). Additionally implements a 'heteroskedasticity-robust' ('HC1') wild bootstrap. Last, the package provides an R binding to 'WildBootTests.jl', which provides additional speed gains and functionality, including the 'WRE' bootstrap for instrumental variable models (based on models of type 'ivreg()' from package 'ivreg') and hypotheses with q > 1.
Last updated 1 years ago
clustered-standard-errorslinear-regression-modelswild-bootstrapwild-cluster-bootstrapopenblascppopenmp
6.68 score 24 stars 2 dependents 111 scripts 227 downloadssummclust - Module to Compute Influence and Leverage Statistics for Regression Models with Clustered Errors
Module to compute cluster specific information for regression models with clustered errors, including leverage and influence statistics. Models of type 'lm' and 'fixest'(from the 'stats' and 'fixest' packages) are supported. 'summclust' implements similar features as the user-written 'summclust.ado' Stata module (MacKinnon, Nielsen & Webb, 2022; <arXiv:2205.03288v1>).
Last updated 2 years ago
clustered-standard-errorsfixestlinear-regressionrobust-inference
6.16 score 6 stars 3 dependents 53 scripts 247 downloadswildrwolf - Fast Computation of Romano-Wolf Corrected p-Values for Linear Regression Models
Fast Routines to Compute Romano-Wolf corrected p-Values (Romano and Wolf (2005a) <DOI:10.1198/016214504000000539>, Romano and Wolf (2005b) <DOI:10.1111/j.1468-0262.2005.00615.x>) for objects of type 'fixest' and 'fixest_multi' from the 'fixest' package via a wild (cluster) bootstrap.
Last updated 1 years ago
fixestmultiple-comparisonsromano-wolfwild-bootstrapwild-cluster-bootstrap
3.54 score 7 stars 1 dependents 33 scripts 30 downloadsJuliaConnectoR.utils - Utility Functions for the JuliaConnectoR
Utility functions for the JuliaConnectoR package for developers and users. Helps users of R packages that link R and Julia via JuliaConnectoR to install Julia and Julia dependencies, to connect R and Julia, to set the number of threads Julia uses from within R and to set a Julia seed from R. Helps developers to set up a github actions workflow to run both R and Julia.
Last updated 3 years ago
2.00 score 2 stars 6 scripts